{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用柱形图分析美国确诊人数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 初始设置\n",
    "\n",
    "首先，导入所需的库，并设置中文字体等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入所需的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as image\n",
    "\n",
    "# 正常显示中文标签\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "# 自动适应布局\n",
    "mpl.rcParams.update({'figure.autolayout': True})\n",
    "\n",
    "# 正常显示负号\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 定义颜色，主色：蓝色，辅助色：灰色，互补色：橙色\n",
    "c = {'蓝色':'#00589F', '深蓝色':'#003867', '浅蓝色':'#5D9BCF',\n",
    "     '灰色':'#999999', '深灰色':'#666666', '浅灰色':'#CCCCCC',\n",
    "     '橙色':'#F68F00', '深橙色':'#A05D00', '浅橙色':'#FBC171'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 定义数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义数据\n",
    "df = pd.DataFrame({'美国确诊人数':['第一个百万', '第二个百万', '第三个百万', '第四个百万'], '所花的天数':[98, 42, 30, 16]})\n",
    "\n",
    "# 画图用的数据定义\n",
    "x = df['美国确诊人数']\n",
    "y = df['所花的天数']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 开始画图\n",
    "\n",
    "用「**面向对象**」的方法画图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用「面向对象」的方法画图\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# 设置标题\n",
    "ax.set_title('\\n 美国百万确诊人数所花的天数越来越短\\n', fontsize=28, loc='left', color=c['深灰色'])\n",
    "\n",
    "# 画柱形图\n",
    "ax.bar(x, y, width=0.6, color=c['蓝色'])\n",
    "\n",
    "# 用箭头强调对比的关系\n",
    "ax.annotate('', xy=(3, 26), xytext=(0.5, 98), arrowprops=dict(color=c['橙色'], connectionstyle=\"arc3,rad=0.25\"))\n",
    "\n",
    "# 设置数据标签\n",
    "for a, b in zip(x, y):\n",
    "    ax.text(a, b, '%.0f' % b, ha='center', va= 'bottom', fontsize=22, color=c['深灰色'])\n",
    "\n",
    "# 隐藏边框\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "# 隐藏 X 轴的刻度线\n",
    "ax.tick_params(axis='x', which='major', length=0)\n",
    "\n",
    "# 隐藏 Y 轴刻度\n",
    "ax.set_yticks([])\n",
    "\n",
    "# 设置坐标标签字体大小和颜色\n",
    "ax.tick_params(labelsize=20, colors=c['深灰色'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3.7.0 64-bit",
   "language": "python",
   "name": "python37064bit2965220bd7d04cdebdae4cbe7c63b822"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
